# Lead Applied Scientist, Search & Information Retrieval at Thomson Reuters

Thomson Reuters is hiring a Lead Applied Scientist, Search & Information Retrieval for a remote role open to applicants in United States. It pays $148k-$274k a year and Level rates it Builds AI ●●●●; you can [apply on Level](https://jobsbylevel.com/go/97b81c36-40dc-4cda-bf82-d5db22fe0f60?utm_medium=ai-agent).

AI Level 4, AI centrality 93 out of 100. Remote (United States of America, New York, New York).

## Details

- Company: [Thomson Reuters](https://jobsbylevel.com/companies/thomson-reuters)
- AI level: AI Level 4 (score 93 out of 100)
- Location: Remote (United States of America, New York, New York)
- Salary: $148k-$274k
- Posted: October 13, 2026
- Apply: https://jobsbylevel.com/go/97b81c36-40dc-4cda-bf82-d5db22fe0f60?utm_medium=ai-agent

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/lead-applied-scientist-search-information-retrieval-at-thomson-reuters-3ba6d8

## Description

Lead Applied Scientist, Search & Information Retrieval Job Description: This role sits within the applied science function. You will own the design, development, and production deployment of large-scale search and information retrieval systems that power Westlaw, Practical Law, CoCounsel, and next-generation Thomson Reuters search experiences. The problems are real, the scale is large, and the expectation is shipped, reliable, measurable impact. You will work across retrieval architectures, indexing pipelines, ranking and re-ranking systems, semantic retrieval, hybrid search, and retrieval optimization for complex legal, tax, and accounting content. Multiple product teams depend on what this function delivers. Key Responsibilities: You hold a PhD in Computer Science, Information Retrieval, Machine Learning, NLP, or a related field, with 8+ years of post-degree industry experience building and deploying search and retrieval systems at scale. You have hands-on depth across indexing, retrieval, ranking, relevance evaluation, and production deployment. You publish, you mentor, and you measure success by what ships and performs in production. You understand search beyond simply consuming vector databases or retrieval APIs. You have built, optimized, and evaluated search systems that solve real user problems. Design and deploy search architectures supporting large-scale legal, tax, and enterprise content collections Build and optimize ingestion pipelines that analyze, enrich, and prepare documents for retrieval Develop ranking and re-ranking systems using both traditional IR techniques and modern LLM-based approaches Improve retrieval quality through semantic retrieval, hybrid retrieval, query understanding, and relevance optimization Design evaluation frameworks for retrieval performance, relevance, ranking quality, and end-user outcomes Lead technical decisions around indexing strategies, retrieval architectures, ranking models, and search infrastructure Partner with engineering teams to deliver scalable, reliable, and performant search services Contribute to the development of self-service search platform capabilities used by internal product teams Provide technical input to senior leadership on search, retrieval, and AI strategy Mentor applied scientists and machine learning practitioners across the organization Required Qualifications: PhD in Computer Science, Information Retrieval, AI, Machine Learning, NLP, or a related field preferred 8+ years of industry experience building production search, information retrieval, ranking, or recommendation systems Publications at SIGIR, ACL, EMNLP, NeurIPS, ICLR, KDD, WWW, or equivalent venues Strong production Python skills and experience with PyTorch, Hugging Face Transformers, and distributed model development Search engine architecture, indexing systems, and ingestion pipelines Ranking and re-ranking systems rather than solely consuming search technologies Information retrieval, semantic retrieval, hybrid retrieval, and vector search architectures Query understanding, relevance optimization, and search evaluation methodologies Retrieval systems supporting large collections of text-rich content LLM-enhanced retrieval, RAG architectures, and retrieval optimization End-to-end measurement and evaluation of search quality and user outcomes Preferred Qualifications: Experience with legal, regulatory, tax, scientific, or other text-heavy domains Building retrieval systems over large enterprise knowledge repositories Experience with Elasticsearch, OpenSearch, Solr, Vespa, or similar search technologies API platform development and self-service search platforms Agentic AI systems that incorporate retrieval capabilities AzureML or AWS SageMaker Experience building systems that combine search, retrieval, and document understanding capabilities #LI-SM2 This posting is for proactive recruitment purposes and may be used to fill current openings or future vacancies within our organization. What’s

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/lead-applied-scientist-search-information-retrieval-at-thomson-reuters-3ba6d8

Source: https://jobsbylevel.com/jobs/lead-applied-scientist-search-information-retrieval-at-thomson-reuters-3ba6d8

## Cite this page

Level. https://jobsbylevel.com/jobs/lead-applied-scientist-search-information-retrieval-at-thomson-reuters-3ba6d8.

Get job alerts: https://jobsbylevel.com/newsletter